In [1]:
# работа с данными
import pandas as pd
import numpy as np
# визуализация данных
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
Банковская гарантия — соглашение, по которому банк обязан выплатить долг клиента, если тот не выполнил своих обязательств. Выдача гарантий банком происходит после одобрения клиентской заявки.¶
Задача — построить аппликационную модель оценки вероятности предъявления по гарантии, чтобы минимизировать потери от кредитного риска. В качестве предикторов для моделирования используются анкетные данные клиента и его финансовые показатели.
In [2]:
# скачиваем данные
!wget https://github.com/dsbattle/hackathon-mkb/raw/master/train_dataset_hackathon_mkb.csv
!wget https://github.com/dsbattle/hackathon-mkb/raw/master/test_dataset_hackathon_mkb.csv
--2021-12-07 09:54:19-- https://github.com/dsbattle/hackathon-mkb/raw/master/train_dataset_hackathon_mkb.csv Connecting to 192.168.64.142:3128... connected. Proxy request sent, awaiting response... 302 Found Location: https://raw.githubusercontent.com/dsbattle/hackathon-mkb/master/train_dataset_hackathon_mkb.csv [following] --2021-12-07 09:54:20-- https://raw.githubusercontent.com/dsbattle/hackathon-mkb/master/train_dataset_hackathon_mkb.csv Connecting to 192.168.64.142:3128... connected. Proxy request sent, awaiting response... 200 OK Length: 9253323 (8.8M) [text/plain] Saving to: ‘train_dataset_hackathon_mkb.csv’ train_dataset_hacka 100%[===================>] 8.82M 9.67MB/s in 0.9s 2021-12-07 09:54:22 (9.67 MB/s) - ‘train_dataset_hackathon_mkb.csv’ saved [9253323/9253323] --2021-12-07 09:54:22-- https://github.com/dsbattle/hackathon-mkb/raw/master/test_dataset_hackathon_mkb.csv Connecting to 192.168.64.142:3128... connected. Proxy request sent, awaiting response... 302 Found Location: https://raw.githubusercontent.com/dsbattle/hackathon-mkb/master/test_dataset_hackathon_mkb.csv [following] --2021-12-07 09:54:22-- https://raw.githubusercontent.com/dsbattle/hackathon-mkb/master/test_dataset_hackathon_mkb.csv Connecting to 192.168.64.142:3128... connected. Proxy request sent, awaiting response... 200 OK Length: 3581209 (3.4M) [text/plain] Saving to: ‘test_dataset_hackathon_mkb.csv’ test_dataset_hackat 100%[===================>] 3.42M 8.11MB/s in 0.4s 2021-12-07 09:54:23 (8.11 MB/s) - ‘test_dataset_hackathon_mkb.csv’ saved [3581209/3581209]
In [3]:
train_df = pd.read_csv('data/train_dataset_hackathon_mkb.csv', sep=';', encoding='cp1251')
test_df = pd.read_csv('data/test_dataset_hackathon_mkb.csv', sep = ';', encoding='cp1251')
print(f'Размеры тренировочной выборки {train_df.shape}, размеры тестовой выборки {test_df.shape}')
Размеры тренировочной выборки (17891, 124), размеры тестовой выборки (7330, 123)
In [5]:
# кол-во пропущенных значений по каждому столбцу
for n, i in enumerate(train_df.isnull().sum()):
print(train_df.isnull().sum().index[n] + ' ' + str(i))
id_contract 0 id_client 0 SIGN_DATE 0 IP_flag 0 TARGET 0 F1100 10955 F1110 16471 F1150 11550 F1160 17545 F1170 16392 F1180 15391 F1190 16371 F1200 6041 F1210 8156 F1220 14593 F1230 6407 F1240 15035 F1250 6281 F1260 13529 F1300 6041 F1310 10187 F1320 17794 F1350 16933 F1360 16522 F1370 10396 F1400 13675 F1410 15475 F1420 15603 F1450 17160 F1500 6277 F1510 12633 F1520 6413 F1530 17139 F1550 16990 F1600 6021 F1700 6022 F2100 10341 F2110 6444 F2120 6943 F2200 6386 F2210 13235 F2220 14937 F2300 6361 F2310 17590 F2320 14971 F2330 14388 F2340 10426 F2350 7184 F2400 6381 F2410 8938 F1150_LAG1 12149 F1230_LAG1 8081 F1410_LAG1 16053 F1510_LAG1 13179 F1520_LAG1 8134 F2110_LAG1 8082 F2120_LAG1 8509 F2200_LAG1 8015 F2210_LAG1 13738 F2220_LAG1 15484 F2300_LAG1 7994 F2320_LAG1 15185 F2330_LAG1 14690 F2400_LAG1 7997 EGRPOINCLUDED 3500 DATEFIRSTREG 1334 OKFS_GROUP 3500 OKOPF_GROUP 3500 OKOGU_GROUP 3503 OKATO_REGIONCODE 1332 OKATO_FED 1336 OKTMO_CODE 1339 OKTMO_FED 1343 WORKERSRANGE 3980 TAXREG_REGDATE 1337 TAXREGPAY_REGDATE 1428 COUNTCOOWNERFCSM 3596 COUNTCOOWNERROSSTAT 3596 COUNTCOOWNEREGRUL 3596 COUNTBRANCH 3596 COUNTBRANCHROSSTAT 3596 COUNTBRANCHEGRUL 3596 TELEPHONECOUNT 3500 MANAGERCOUNTINCOUNTRY 3500 MANAGERCOUNTINREGION 3500 MANAGERINNCOUNT 3500 OKVED_CODE 1199 PLAINTIFF_CASESNUMBER_YEAR 13959 PLAINTIFF_SUM_YEAR 13959 DEFENDANT_CASESNUMBER_YEAR 13959 DEFENDANT_SUM_YEAR 13959 THIRDOROTHERPERSON_YEAR 13959 PLAINTIFF_CASESNUMBER_EVER 12749 PLAINTIFF_SUM_EVER 12749 DEFENDANT_CASESNUMBER_EVER 12749 DEFENDANT_SUM_EVER 12749 THIRDOROTHERPERSON_EVER 12749 ADMITTEDNUMBER_233_YEAR 13595 NOTADMITTEDNUMBER_233_YEAR 13595 WINNERNUMBER_233_YEAR 13595 SIGNEDNUMBER_233_YEAR 13595 SUM_233_YEAR 13595 ADMITTEDNUMBER_233_EVER 12665 NOTADMITTEDNUMBER_233_EVER 12665 WINNERNUMBER_233_EVER 12665 SIGNEDNUMBER_233_EVER 12665 SUM_233_EVER 12665 ADMITTEDNUMBER_95_YEAR 11560 NOTADMITTEDNUMBER_95_YEAR 11560 WINNERNUMBER_95_YEAR 11560 SIGNEDNUMBER_95_YEAR 11560 SUM_95_YEAR 11560 ADMITTEDNUMBER_95_EVER 11107 NOTADMITTEDNUMBER_EVER 11107 WINNERNUMBER_95_EVER 11107 SIGNEDNUMBER_95_EVER 11107 SUM_95_EVER 11107 FLAG_DISQUALIFICATION 17886 COUNT_CHANGE_YEAR 17652 COUNT_CHANGE_EVER 16382 BIRTHDATE 16472 AGE 16472 SEX_NAME 15723 CITIZENSHIP_NAME 15763
In [6]:
# общее кол-во пустых значений
train_df.isnull().sum().sum()
Out[6]:
1246545
In [7]:
# визуализация данных
train_df.head()
Out[7]:
| id_contract | id_client | SIGN_DATE | IP_flag | TARGET | F1100 | F1110 | F1150 | F1160 | F1170 | ... | WINNERNUMBER_95_EVER | SIGNEDNUMBER_95_EVER | SUM_95_EVER | FLAG_DISQUALIFICATION | COUNT_CHANGE_YEAR | COUNT_CHANGE_EVER | BIRTHDATE | AGE | SEX_NAME | CITIZENSHIP_NAME | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1847 | 01JAN2018:00:00:00 | 0 | 0 | 1.298961e+09 | 2154000.0 | 1.125573e+09 | NaN | 150010000.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 2 | 4650 | 01JAN2018:00:00:00 | 1 | 0 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 3 | 4770 | 01JAN2018:00:00:00 | 0 | 0 | 7.337400e+07 | NaN | 7.337400e+07 | NaN | NaN | ... | 169.0 | 168.0 | 18351739.0 | NaN | NaN | 1.0 | NaN | NaN | NaN | NaN |
| 3 | 4 | 12237 | 01JAN2018:00:00:00 | 0 | 0 | 1.937488e+09 | 122828000.0 | 6.103280e+08 | NaN | 809426000.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 5 | 9988 | 01JAN2018:00:00:00 | 1 | 0 | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 124 columns
In [8]:
train_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 17891 entries, 0 to 17890 Columns: 124 entries, id_contract to CITIZENSHIP_NAME dtypes: float64(108), int64(4), object(12) memory usage: 16.9+ MB
In [9]:
# визуализация таргета
train_df['TARGET'].value_counts().plot(kind = 'bar', color = ['blue', 'red'])
plt.title('Кол-во данных таргета');
Обработка и генерация новых признаков¶
In [10]:
# забираем с даты только год
import re
def data_convert(data):
return 0 if pd.isna(data) else int(re.findall('\d{4}', data)[0])
for item in ['DATEFIRSTREG', 'TAXREG_REGDATE', 'TAXREGPAY_REGDATE', 'SIGN_DATE']:
train_df[item + 'all_year'] = train_df[item].apply(data_convert)
for item in ['DATEFIRSTREG', 'SIGN_DATE', 'TAXREGPAY_REGDATE', 'TAXREG_REGDATE']:
test_df[item + 'all_year'] = test_df[item].apply(data_convert)
In [11]:
# Заменяем "Регистрация в налоговом органе. Дата регистрации" в 1960 году на дату регистрации ФНС.
# Выглядит более логично
train_df['TAXREGPAY_REGDATE' + 'all_year'] = train_df[['TAXREGPAY_REGDATEall_year', 'TAXREG_REGDATEall_year']].apply(lambda x: x[1] \
if int(x[1]) > 1960 else x[0], axis=1)
test_df['TAXREGPAY_REGDATE' + 'all_year'] = test_df[['TAXREGPAY_REGDATEall_year', 'TAXREG_REGDATEall_year']].apply(lambda x: x[1] \
if x[1] > 1960 else x[0], axis=1)
In [12]:
def makeX(data):
# предобработка данных
data['CITIZENSHIP_NAME'] = data['CITIZENSHIP_NAME'].fillna(-1).map({-1: -1, 'Российская Федерация': 4, 'Таджикистан': 3, 'Казахстан': 2, 'Армения': 1})
data['SEX_NAME'] = data['SEX_NAME'].fillna(0).map({0: 0, 'мужской': 1, 'женский': -1})
group_names = ['OKFS_GROUP', 'OKOPF_GROUP', 'OKOGU_GROUP'] + ['OKVED_CODE']
date_names = ['SIGN_DATE', 'DATEFIRSTREG', 'TAXREG_REGDATE', 'TAXREGPAY_REGDATE', 'BIRTHDATE']
for name in group_names + date_names + ['id_client']:
data[name] = data[name].fillna(-1)
tmp = data[name].value_counts()
tmp = tmp + 0.1 * np.random.randn(len(tmp))
data[name] = data[name].map(tmp)
return data
In [13]:
train = makeX(train_df) # обрабатываем обучение
test = makeX(test_df) # обрабатываем тест
In [14]:
# сохраняем ид_клиента для записи
id_client = pd.DataFrame()
id_client['id_contract'] = test['id_contract']
id_client.head()
Out[14]:
| id_contract | |
|---|---|
| 0 | 17892 |
| 1 | 17893 |
| 2 | 17894 |
| 3 | 17895 |
| 4 | 17896 |
In [15]:
# Замена строки на числа
for col in ['OKFS_GROUP', 'OKOPF_GROUP', 'OKOGU_GROUP', 'OKVED_CODE']:
train[col] = train[col].astype('category')
train[col] = train[col].cat.codes
test[col] = test[col].astype('category')
test[col] = test[col].cat.codes
In [16]:
# Замена нулевых значений на более полулярные
def _list_max_key(mass, dataset):
result = {}
for item in mass:
result[item] = {np.nan:pd.crosstab(dataset[item], 1).sort_values(by = 1)[::-1].index[0]}
return result
a = ['COUNTCOOWNERFCSM', 'COUNTCOOWNERROSSTAT', 'COUNTCOOWNEREGRUL', 'COUNTBRANCH',
'COUNTBRANCHROSSTAT', 'COUNTBRANCHEGRUL', 'OKATO_REGIONCODE', 'OKATO_FED', 'OKTMO_FED',
'OKFS_GROUP', 'OKOPF_GROUP', 'OKOGU_GROUP', 'IP_flag']
dick_train = _list_max_key(a ,train)
dick_test = _list_max_key(a, test)
train = train.replace(dick_train)
test = test.replace(dick_test)
In [17]:
# численность персонала, приводим к числу
# def convert_worker(data):
# return max([int(i) for i in data.split(' ') if i.isdigit()], default = 1)
# train['WORKERSRANGE'] = train['WORKERSRANGE'].astype('str')
# train['WORKERSRANGE'] = train['WORKERSRANGE'].apply(convert_worker)
# test['WORKERSRANGE'] = test['WORKERSRANGE'].astype('str')
# test['WORKERSRANGE'] = test['WORKERSRANGE'].apply(convert_worker)
str2num = {'5000 и более': 12, '1001 .. 5000': 11, '501 .. 1000': 10, '251 .. 500': 9, '201 .. 250': 8, '151 .. 200': 7,
'101 .. 150': 6, '51 .. 100': 5, '16 .. 50': 4, '11 .. 15': 3, '6 .. 10': 2, '0 .. 5': 1}
train['WORKERSRANGE'] = train['WORKERSRANGE'].map(str2num).astype(float)
test['WORKERSRANGE'] = test['WORKERSRANGE'].map(str2num).astype(float)
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_ = pd.crosstab(train['WORKERSRANGE'], 1)
fig, ax = plt.subplots(figsize = (12, 10))
sns.barplot(list(_.index), list(_[1].values))
ax.set_title('Кол-во сотрудников в компаниях по группам')
ax.set_xlabel('Группы сотрудников')
ax.set_ylabel('Кол-во групп');
/home/andrey/Neural/neuralenv/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. FutureWarning
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def money(data):
data['debt'] = (data['F1410']+data['F1510'])
data['debt_on_profit'] = (data['F1410']+data['F1510'])/data['F2110']
data['debt_on_profit_LAG1'] = (data['F1410_LAG1']+data['F1510_LAG1'])/data['F2110_LAG1']
data['debt_on_profit_diff'] = data['debt_on_profit'] - data['debt_on_profit_LAG1']
data['debt_on_profit_diff_ratio'] = (data['debt_on_profit'] - data['debt_on_profit_LAG1'])/data['debt_on_profit']
lags = [ 'F1150_LAG1', 'F1230_LAG1', 'F1410_LAG1', 'F1510_LAG1', 'F1520_LAG1', 'F2110_LAG1', 'F2120_LAG1',
'F2200_LAG1', 'F2210_LAG1', 'F2220_LAG1', 'F2300_LAG1', 'F2320_LAG1', 'F2330_LAG1', 'F2400_LAG1']
for lag in lags:
col = lag.split('_')[0]
data[col + '_delta_ratio'] = (data[col] - data[lag])/data[col]
data['fit_on_all']=data['F2200']/data['F2110']
data['in'] = data['F1300'] + data['F2120'] + data['F2110'] + data['F1210'] + data['F2300']
data['out'] = data['F1250'] + data['F1230'] + data['F1520'] + data['F1510'] + data['F2410']
data['waste'] = data['F2350'] + data['F2210']
data['in_on_out'] = data['in']/data['out']
data['in_on_waste'] = data['in']/data['waste']
data['in_on_waste_out'] = data['in']/(data['waste'] + data['out'])
data['F_contract_count'] = data[['id_client','id_contract']].groupby('id_client', as_index=False).transform('count')
data['F_contract_count_in_day'] = data.groupby(['id_client','SIGN_DATE'])['SIGN_DATE'].transform('count')
return data
train = money(train)
test = money(test)
In [20]:
# замена пустых значений на 0
train.fillna(0, inplace=True)
test.fillna(0, inplace = True)
In [21]:
# кол-во пустых значений
train.isnull().sum().sum(), test.isnull().sum().sum()
Out[21]:
(0, 0)
In [22]:
corr_matrix = train.corr()
fig, ax = plt.subplots(figsize = (100, 45))
ax = sns.heatmap(corr_matrix,
annot = True,
linewidths=0.5,
fmt = '.2f',
cmap = 'BuGn'
);
plt.figure(figsize = (20, 12))
Out[22]:
<Figure size 1440x864 with 0 Axes>
<Figure size 1440x864 with 0 Axes>
Тренировка моделей Catboosting¶
In [23]:
from sklearn.model_selection import (GridSearchCV,
train_test_split,
StratifiedKFold)
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, \
RandomForestRegressor, AdaBoostClassifier
from catboost import CatBoostRegressor, CatBoostClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, accuracy_score, recall_score, \
precision_score, f1_score, classification_report, plot_roc_curve
from sklearn.metrics import roc_curve,confusion_matrix
from xgboost import XGBClassifier
In [24]:
# тут мы делим данные на тренировку и валидацию 80 на 20. Нужно для проверки модели
np.random.seed(42)
X = train.drop('TARGET', axis = 1)
y = train['TARGET']
df_X_train, df_X_test, df_y_train, df_y_test = train_test_split(X, y, test_size=0.2, random_state=42)
df_X_train.shape, df_y_train.shape
Out[24]:
((14312, 155), (14312,))
In [25]:
# тренировка модели
model = CatBoostClassifier().fit(df_X_train, df_y_train)
plot_roc_curve(model, df_X_test, df_y_test)
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Out[25]:
<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7ffa5c3f2a20>
In [26]:
kek_pred = model.predict(df_X_test)
print("Roc_auc_score = " + str(roc_auc_score(df_y_test, kek_pred)))
Roc_auc_score = 0.8997710903099757
In [27]:
# тренировка лучших моделей
np.random.seed(42)
model = CatBoostClassifier().fit(X, y)
test['TARGET'] = np.log(model.predict_proba(test)[:, 1])
test[['id_contract', 'TARGET']].to_csv('submit_file_last.csv', sep=';', index=False)
test[['id_contract', 'TARGET']].head()
test = test.drop('TARGET', axis = 1)
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